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Storage systems are deemed to be unable to provide revenue commensurate with the resources invested in them, thus discouraging their incorporation within power networks. In prosumer microgrids, storage systems can provide revenue from reduced grid consumption, energy arbitraging or when serving as back-up power. This study aims to examine stacking these revenue streams with the aim of making storage systems financially viable for inclusion in prosumer microgrids.

With the aim of reducing self-consumption and maximising revenue, the prosumer microgrid incorporating hybrid energy storage systems (HESS) and solar PV power is solved using the CPLEX solver of the Advanced Interactive Multidimensional Modeling Software (AIMMS). The financial analysis of the results is carried out to provide the payback periods of different system configurations of the prosumer microgrid.

The findings reveal that the payback period of the three HESS when minimising grid expenses during self-consumption alone and when compared with stacked revenue streams shows an improvement from 4.8–11.2 years to 2.4–6.6 years. With stacked HESS revenues, the supercapacitor-lithium ion battery HESS gave the shortest payback period of 2.31 years when solar PV power is at 75% penetration level.

Existing literature has considered revenue streams of storage systems at the electrical power transmission and distribution levels, but not for prosumer microgrids in particular. This study has captured these benefits and verified the profitability of stacking revenue from HESS to prosumer microgrids, using a case study.

In this paper, a new method to improve the performance of particle swarm optimization is proposed.

This paper introduces hypothesis testing to determine whether the particles trap into the local minimum or not, then special re-initialization was proposed, finally, some famous benchmarks and constrained engineering optimization problems were used to test the efficiency of the proposed method. In the revised manuscript, the content was revised and more information was added.

The proposed method can be easily applied to PSO or its varieties. Simulation results show that the proposed method effectively enhances the searching quality.

This paper proposes an adaptive particle swarm optimization method (APSO). A technique is applied to improve the global optimization performance based on the hypothesis testing. The proposed method uses hypothesis testing to determine whether the particles are trapped into local minimum or not. This research shows that the proposed method can effectively enhance the searching quality and stability of PSO.

Asia's traditional experiences with care provision differ considerably from those of the West given the prevalent family‐based social norms about care and policies in this region. In recent decades, Asia has experienced profound social and demographic transformations and is thus faced with significant challenges around care. However, care in Asian countries is a relatively less studied topic. There is an urgent need for a comparative study on recent policy and practice changes in care for the elderly and young children in different regions of Asia. The purpose of this special issue is to examine complicated boundary shift in care provision and financing between the state, market, community and family in East, Southeast and South Asia and to explore the implications of these changes in care policies and practices for social stratification by class and gender in Asia.

The introduction to this special issue gives an overview of the social and demographic transformations and new strains on care in Asia as a background and introduces the framework of welfare mix employed in this special issue, especially the concepts of social care and the welfare/care diamond.

The introduction summarizes the variations in regard to the governance and provision of care between different Asian countries and compares the differences in the state involvement between Asia and Europe.

The authors also discuss some of their contributions to methodological approaches and analytical frameworks in studying care and the implications of the current research for future studies.

This paper aims to investigate how cathodic polarization behavior significantly affects the selection of cathodic protection parameters and the effectiveness of protecting underwater metal structures. Factors such as water depth and operating conditions impact seawater temperature, making it crucial to understand the effects of temperature on cathodic protection parameters for underwater pipelines.

In this paper, potentiostatic polarization was carried out by three-electrode method, and morphology, X-ray diffraction and electrochemical analysis.

It was determined that the stable current densities at the minimum negative potential (−0.8 V_SSC) for pipeline steel varied at different temperatures: 7°C, room temperature and 60°C. The cathodic protection potential corresponding to the lowest stable current density was observed to be −1.0 V_SSC at 7°C and −0.95 V_SSC at room temperature and 60°C.

This study elucidates the mechanisms by which different temperatures affect the protective performance of calcareous deposits and current densities.

The purpose of this paper is to investigate in the context of the persistent low fertility that contributes to an ageing society, what might be the shifting roles of state and family in caring for children?

This paper aims to address this research question by drawing on the conceptual framework of “social care” (Daly and Lewis), and on data collected through a qualitative study concerning the state's population policies aimed at encouraging childbearing among citizens in Singapore.

Three themes from the interview data relate to the various dimensions of care: first, in terms of care‐as‐responsibility, interviewees consider childbearing a long‐term commitment. In this context, they perceive the current Baby Bonus scheme only as a short‐term benefit, having limited effects. Second, regarding care‐as‐costs, interviewees pointed out that some important social services are not universally affordable. In particular, they expressed a need for more state funding to put education and healthcare within the reach of the general public. Third, in terms of care‐as‐labour, care‐giving for young children by family members continues to be seen as ideal. However, there is a gap between such an ideal and the reality.

These findings suggest a greater financial and regulatory role for the state in childcare provision is increasingly vital.

As it stands, there is a mismatch of people's expectations and available policy initiatives, and this mismatch possibly undermines the success of the government's policy of encouraging childbearing.

This research complements existing studies based on content analysis of policy or statistical analysis of survey data.

The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.

A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.

The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.

MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

As artificial intelligence technology empowers service robots, they increasingly communicate with consumers in a human-like manner. This study aims to investigate the effect of service robots’ different conversational styles (competent conversational style vs. cute conversational style) on consumer service acceptance and demonstrate the moderating role of consumers’ technology anxiety.

Based on anthropomorphism theory and social presence theory, the authors conducted two scenario-based experiments (restaurant scenario and hotel scenario) to investigate this issue.

The results indicate that service robots’ conversational styles impact consumers’ willingness to accept the use of service robots through perceived social presence and positive emotion. Moreover, consumers perceived social presence and positive emotion play a serial mechanism. In addition, the effect of competent conversational style on consumers perceived social presence is less effective than that of cute conversational style. Finally, the authors demonstrate the moderating role of consumer technology anxiety in the relationship between conversational styles and perceived social presence.

To provide consumers with a positive human–robot interaction experience at the service front line, managers need to make better use of the conversational styles of service robots by comprehensively considering the characteristics of consumer technology anxiety.

This research extends the literature on service robots by integrating consumer characteristics and robots’ conversational styles. These findings highlight the effectiveness of cute conversational style in alleviating consumer technology anxiety.

The product family assembly line (PFAL) is a mixed model-assembly line, which is widely used in mass customization and intelligent manufacturing. The purpose of this paper is to study the problem of PFAL, a flexible (evolution) planning method to respond to product evolution for PFAL, to focus on product data analysis and evolution planning method.

The evolution balancing model for PFAL is established and an improved NSGA_II (INSGA_II) is proposed. From the perspective of data analysis, dynamic characteristics of PFAL are researched and analyzed. Especially the tasks, which stability is considered, can be divided into a platform and individual task. In INSGA_II algorithm, a new density selection and a decoding method based on sorting algorithms are proposed to compensate for the lack of traditional algorithms.

The effectiveness and feasibility of the method are validated by an example of PFAL evolution planning for a family of similar mechanical products. The optimized efficiency is significantly improved using INSGA_II proposed in this paper and the evolution planning model proposed has a stronger ability to respond to product evolution, which maximizes business performance over an effective period of time.

The assembly line designers and managers in discrete manufacturing companies can obtain an optimal solution for PFAL planning through the evolution planning model and INSGA-II proposed in this paper. Then, this planning model and optimization method have been successfully applied in the production of small wheel loaders.

The purpose of this study is to investigate whether the corporate environmental, social and governance (ESG) performance of enterprise is influenced by the enterprise digital transformation. In addition, this study explains how enterprise digital transformation affects ESG performance.

The sample covers 4,646 nonfinancial companies listed on China’s A-share market from 2009 to 2021. The study adopts the fixed-effects multiple linear regression to perform the data analysis.

The study finds that enterprise digital transformation has a significant inverted U-shaped impact on ESG performance. Moderate digital transformation can improve enterprise ESG performance, whereas excessive digital transformation will bring new organizational conflicts and increase enterprise costs, which is detrimental to ESG performance. This inverted U-shaped effect is more pronounced in industrial cities, manufacturing industries and enterprises with less financing constraints and executives with financial backgrounds. Enterprise digital transformation mainly affects ESG performance by affecting the level of internal information communication and disclosure, the level of internal control and the principal-agent cost.

The government should take multiple measures to encourage enterprises to choose appropriate digital transformation based on their own production behaviors and development strategies, encourage them to innovate and upgrade their organizational management and development models in conjunction with digital transformation and guide them to use digital technology to improve ESG performance.

This study shows that irrational digital transformation cannot effectively improve the ESG performance of enterprises and promote the sustainable development of the country. Enterprises should carry out reasonable digital transformation according to their own development needs and finally improve the green and sustainable development ability of enterprises and promote the sustainable development of society.

This study examines the relationship between enterprise digital transformation and ESG performance. Different from the linear relationship between the two in previous major studies, this study proves the inverse U-shaped relationship between enterprise digital transformation and ESG performance through mathematical theoretical model derivation and empirical test. This study also explores in detail how corporate digital transformation affects ESG performance, as well as discusses heterogeneity at the city, industry and firm levels. It is proposed that enterprises should take into account their own characteristics and carry out reasonable digital transformation according to their development needs.

This paper aims to explore the development trend of OA articles and their advantages and disadvantages in the process of fighting the pandemic, and conduct a multi-level and multi-angle analysis of the relationship between publishing costs and the influence of OA articles.

This study first compares the total number of articles in Web of Science with the number of OA articles, and the total number of COVID-19 related articles with the total number of OA articles. Subsequently, using the methods of institutional cooperation co-occurrence network, keyword co-occurrence and multidimensional scale analysis, and using the literature on the topic of COVID-19 in CNKI (Chinese National Knowledge Infrastructure) as the data set, we generate visualized maps of research results distribution and keyword co-occurrence network with the help of the Statistical Analysis Toolkit for Infometrics (SATI)

The research results show that the citation frequency and use frequency of OA articles related to COVID-19 are significantly higher than that of non-OA articles. OA articles dominate in the anti-pandemic process, with a series of advantages such as short review cycle, timeliness, high social benefit, high participation and fast dissemination playing an important role. Under the model of author's non-payment for OA article, the degree of institutional cooperation and author cooperation is enhanced, which improves the fluidity of knowledge, strengthens close links between keywords and enhances significant academic influence; OA articles will continue to promote research in the field of COVID-19, but the lack of quality of some OA articles may hinder their development. Then OA articles will further focus on clinical medicine, and related results will continue to promote the development and communication of OA articles in this field.

Corresponding measures are also proposed for the existing problems of OA articles, to provide a reference for the publication and dissemination of OA articles in public health emergencies in the future.